Physics Tip Sheet #2 - February 27, 2002

Being able to build complex atomic structures from the ground up has always seemed a formidable task but researchers have now demonstrated how to use laser fields to accomplish this task. The team created a holographic crystal that generated a complicated light field when a laser was shone through it. Then atoms passing through the light field were deflected and directed into a desired pattern on gold film. The holographic crystal can simultaneously store instructions that lead to a variety of end patterns, any of which can be selected by the researchers.

Activated carbon, made by charring and steaming olive pits, contains a fractal network of channels about 2nm wide. The walls of the channels have a surface area of about 1000 square meters (one football field) per gram, which may lead to new applications in methane and electricity storage. The narrow size of the pores may provide a means for efficient gas separation.

3) Evolution of “constants”: has the speed of light or the electric charge changed? arXiv preprint server

Recent astronomical observations have shown that the fine structure constant, which includes the ratio of the speed of light and the charge of an electron, has increased over time. However, one of these factors could be truly constant and the other changing. A new analysis shows how to work out just which factor has changed based on experimental evidence.

4) Looking for the graininess of spacetime arXiv preprint server, to appear in Astrophysical Journal Letters

A growing proportion of physicists believe that space and time at the most fundamental are grainy, coming in discrete chunks that cannot be broken down any smaller. Perceiving this graininess is a formidable challenge and only indirect techniques seem to be possible. A new analysis predicts that the graininess of spacetime leaves a signature in high-energy cosmic and gamma-ray data and that observations made are indeed consistent with a grainy spacetime.

Just as classical neural networks seem adept at tasks difficult for a conventional (algorithmic) computer, quantum neural networks are able to complete tasks impossible on quantum algorithmic computers (or classical computers and neural nets). This opens the possibility that quantum computers will be useful for many more tasks than currently known to be possible.

Sometimes we need to make a decision about storing or transmitting data sooner than we would like. Quantum resources allow us to delay a decision about what data is needed by storing more than one classical bit of information in each quantum bit. The tradeoff is that once we choose which data to extract, the rest is lost forever.